
"""simple docstring"""


from typing import Optional, Union

import torch
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss

from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithPoolingAndNoAttention, ImageClassifierOutputWithNoAttention
from ...modeling_utils import PreTrainedModel
from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
from .configuration_mobilenet_va import MobileNetVaConfig


SCREAMING_SNAKE_CASE			  =  logging.get_logger(__name__)


# General docstring
SCREAMING_SNAKE_CASE			  =  """MobileNetV1Config"""

# Base docstring
SCREAMING_SNAKE_CASE			  =  """google/mobilenet_v1_1.0_224"""
SCREAMING_SNAKE_CASE			  =  [1, 1_024, 7, 7]

# Image classification docstring
SCREAMING_SNAKE_CASE			  =  """google/mobilenet_v1_1.0_224"""
SCREAMING_SNAKE_CASE			  =  """tabby, tabby cat"""


SCREAMING_SNAKE_CASE			  =  [
    """google/mobilenet_v1_1.0_224""",
    """google/mobilenet_v1_0.75_192""",
    # See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
]



def 						lowerCamelCase__      (				UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_=None					)->				int:







       """simple docstring"""
       UpperCamelCase       			=		{}

       if isinstance(UpperCAmelCase_		,   UpperCAmelCase_					):
              UpperCamelCase       			=		model.mobilenet_va
       else:
              UpperCamelCase       			=		model

       UpperCamelCase       			=		"MobilenetV1/Conv2d_0/"
       UpperCamelCase       			=		backbone.conv_stem.convolution.weight
       UpperCamelCase       			=		backbone.conv_stem.normalization.bias
       UpperCamelCase       			=		backbone.conv_stem.normalization.weight
       UpperCamelCase       			=		backbone.conv_stem.normalization.running_mean
       UpperCamelCase       			=		backbone.conv_stem.normalization.running_var

       for i in range(13					):
              UpperCamelCase       			=		i + 1
              UpperCamelCase       			=		i * 2

              UpperCamelCase       			=		backbone.layer[pt_index]
              UpperCamelCase       			=		F"MobilenetV1/Conv2d_{tf_index}_depthwise/"
              UpperCamelCase       			=		pointer.convolution.weight
              UpperCamelCase       			=		pointer.normalization.bias
              UpperCamelCase       			=		pointer.normalization.weight
              UpperCamelCase       			=		pointer.normalization.running_mean
              UpperCamelCase       			=		pointer.normalization.running_var

              UpperCamelCase       			=		backbone.layer[pt_index + 1]
              UpperCamelCase       			=		F"MobilenetV1/Conv2d_{tf_index}_pointwise/"
              UpperCamelCase       			=		pointer.convolution.weight
              UpperCamelCase       			=		pointer.normalization.bias
              UpperCamelCase       			=		pointer.normalization.weight
              UpperCamelCase       			=		pointer.normalization.running_mean
              UpperCamelCase       			=		pointer.normalization.running_var

       if isinstance(UpperCAmelCase_		,   UpperCAmelCase_					):
              UpperCamelCase       			=		"MobilenetV1/Logits/Conv2d_1c_1x1/"
              UpperCamelCase       			=		model.classifier.weight
              UpperCamelCase       			=		model.classifier.bias

       return tf_to_pt_map



def 						lowerCamelCase__      (				UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_					)->				List[str]:







       """simple docstring"""
       try:
              import numpy as np
              import tensorflow as tf
       except ImportError:
              logger.error(
                  "Loading a TensorFlow models in PyTorch, requires TensorFlow to be installed. Please see "
                  "https://www.tensorflow.org/install/ for installation instructions."					)
              raise

       # Load weights from TF model
       UpperCamelCase       			=		tf.train.list_variables(UpperCAmelCase_					)
       UpperCamelCase       			=		{}
       for name, shape in init_vars:
              logger.info(F"Loading TF weight {name} with shape {shape}"					)
              UpperCamelCase       			=		tf.train.load_variable(UpperCAmelCase_		,   UpperCAmelCase_					)
              UpperCamelCase       			=		array

       # Build TF to PyTorch weights loading map
       UpperCamelCase       			=		_build_tf_to_pytorch_map(UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_					)

       for name, pointer in tf_to_pt_map.items():
              logger.info(F"Importing {name}"					)
              if name not in tf_weights:
                     logger.info(F"{name} not in tf pre-trained weights, skipping"					)
                     continue

              UpperCamelCase       			=		tf_weights[name]

              if "depthwise_weights" in name:
                     logger.info("Transposing depthwise"					)
                     UpperCamelCase       			=		np.transpose(UpperCAmelCase_		,   (2, 3, 0, 1)					)
              elif "weights" in name:
                     logger.info("Transposing"					)
                     if len(pointer.shape					) == 2:  # copying into linear layer
                            UpperCamelCase       			=		array.squeeze().transpose()
                     else:
                            UpperCamelCase       			=		np.transpose(UpperCAmelCase_		,   (3, 2, 0, 1)					)

              if pointer.shape != array.shape:
                     raise ValueError(F"Pointer shape {pointer.shape} and array shape {array.shape} mismatched"					)

              logger.info(F"Initialize PyTorch weight {name} {array.shape}"					)
              UpperCamelCase       			=		torch.from_numpy(UpperCAmelCase_					)

              tf_weights.pop(UpperCAmelCase_		,   UpperCAmelCase_					)
              tf_weights.pop(name + "/RMSProp"		,   UpperCAmelCase_					)
              tf_weights.pop(name + "/RMSProp_1"		,   UpperCAmelCase_					)
              tf_weights.pop(name + "/ExponentialMovingAverage"		,   UpperCAmelCase_					)

       logger.info(F"Weights not copied to PyTorch model: {', '.join(tf_weights.keys()					)}"					)
       return model



def 						lowerCamelCase__      (				UpperCAmelCase_		,   UpperCAmelCase_					)->				torch.Tensor:







       """simple docstring"""
       UpperCamelCase			,							UpperCamelCase       			=		features.shape[-2:]
       UpperCamelCase			,							UpperCamelCase       			=		conv_layer.stride
       UpperCamelCase			,							UpperCamelCase       			=		conv_layer.kernel_size

       if in_height % stride_height == 0:
              UpperCamelCase       			=		max(kernel_height - stride_height		,   0					)
       else:
              UpperCamelCase       			=		max(kernel_height - (in_height % stride_height)		,   0					)

       if in_width % stride_width == 0:
              UpperCamelCase       			=		max(kernel_width - stride_width		,   0					)
       else:
              UpperCamelCase       			=		max(kernel_width - (in_width % stride_width)		,   0					)

       UpperCamelCase       			=		pad_along_width // 2
       UpperCamelCase       			=		pad_along_width - pad_left
       UpperCamelCase       			=		pad_along_height // 2
       UpperCamelCase       			=		pad_along_height - pad_top

       UpperCamelCase       			=		(pad_left, pad_right, pad_top, pad_bottom)
       return nn.functional.pad(UpperCAmelCase_		,   UpperCAmelCase_		,   "constant"		,   0.0					)



class 	__a	(   nn.Module						):






       def __init__(	self			:				List[Any]							,   UpperCAmelCase_			:				MobileNetVaConfig							,   UpperCAmelCase_			:				int							,   UpperCAmelCase_			:				int							,   UpperCAmelCase_			:				int							,   UpperCAmelCase_			:				Optional[int] = 1							,   UpperCAmelCase_			:				Optional[int] = 1							,   UpperCAmelCase_			:				bool = False							,   UpperCAmelCase_			:				Optional[bool] = True							,   UpperCAmelCase_			:				Optional[bool or str] = True							,   )->	None:







              """simple docstring"""
              super().__init__()
              UpperCamelCase       			=		config

              if in_channels % groups != 0:
                     raise ValueError(f"Input channels ({in_channels}) are not divisible by {groups} groups."      )
              if out_channels % groups != 0:
                     raise ValueError(f"Output channels ({out_channels}) are not divisible by {groups} groups."      )

              UpperCamelCase       			=		0 if config.tf_padding else int((kernel_size - 1) / 2      )

              UpperCamelCase       			=		nn.Convad(
                  in_channels=UpperCAmelCase_							,   out_channels=UpperCAmelCase_							,   kernel_size=UpperCAmelCase_							,   stride=UpperCAmelCase_							,   padding=UpperCAmelCase_							,   groups=UpperCAmelCase_							,   bias=UpperCAmelCase_							,   padding_mode="zeros"							,   )

              if use_normalization:
                     UpperCamelCase       			=		nn.BatchNormad(
                         num_features=UpperCAmelCase_							,   eps=config.layer_norm_eps							,   momentum=0.9997							,   affine=UpperCAmelCase_							,   track_running_stats=UpperCAmelCase_							,   )
              else:
                     UpperCamelCase       			=		None

              if use_activation:
                     if isinstance(UpperCAmelCase_							,   UpperCAmelCase_      ):
                            UpperCamelCase       			=		ACTaFN[use_activation]
                     elif isinstance(config.hidden_act							,   UpperCAmelCase_      ):
                            UpperCamelCase       			=		ACTaFN[config.hidden_act]
                     else:
                            UpperCamelCase       			=		config.hidden_act
              else:
                     UpperCamelCase       			=		None






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Dict							,   UpperCAmelCase_			:				torch.Tensor      )->	torch.Tensor:







              """simple docstring"""
              if self.config.tf_padding:
                     UpperCamelCase       			=		apply_tf_padding(UpperCAmelCase_							,   self.convolution      )
              UpperCamelCase       			=		self.convolution(UpperCAmelCase_      )
              if self.normalization is not None:
                     UpperCamelCase       			=		self.normalization(UpperCAmelCase_      )
              if self.activation is not None:
                     UpperCamelCase       			=		self.activation(UpperCAmelCase_      )
              return features





class 	__a	(   _lowerCAmelCase						):
       UpperCamelCase_		:       List[str]				=      MobileNetVaConfig
       UpperCamelCase_		:       Dict				=      load_tf_weights_in_mobilenet_va
       UpperCamelCase_		:       List[str]				=      '''mobilenet_v1'''
       UpperCamelCase_		:       Optional[int]				=      '''pixel_values'''
       UpperCamelCase_		:       List[Any]				=      False






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Any							,   UpperCAmelCase_			:				Union[nn.Linear, nn.Convad]      )->	None:







              """simple docstring"""
              if isinstance(UpperCAmelCase_							,   (nn.Linear, nn.Convad)      ):
                     module.weight.data.normal_(mean=0.0							,   std=self.config.initializer_range      )
                     if module.bias is not None:
                            module.bias.data.zero_()
              elif isinstance(UpperCAmelCase_							,   nn.BatchNormad      ):
                     module.bias.data.zero_()
                     module.weight.data.fill_(1.0      )





SCREAMING_SNAKE_CASE			  =  R"""
    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it
    as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
    behavior.

    Parameters:
        config ([`MobileNetV1Config`]): Model configuration class with all the parameters of the model.
            Initializing with a config file does not load the weights associated with the model, only the
            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""

SCREAMING_SNAKE_CASE			  =  R"""
    Args:
        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
            [`MobileNetV1ImageProcessor.__call__`] for details.
        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
            more detail.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""



@add_start_docstrings(
    '''The bare MobileNetV1 model outputting raw hidden-states without any specific head on top.'''     ,  _lowerCAmelCase     ,  )
class 	__a	(   _lowerCAmelCase						):






       def __init__(	self			:				str							,   UpperCAmelCase_			:				MobileNetVaConfig							,   UpperCAmelCase_			:				bool = True      )->	Optional[int]:







              """simple docstring"""
              super().__init__(UpperCAmelCase_      )
              UpperCamelCase       			=		config

              UpperCamelCase       			=		32
              UpperCamelCase       			=		max(int(depth * config.depth_multiplier      )							,   config.min_depth      )

              UpperCamelCase       			=		MobileNetVaConvLayer(
                  UpperCAmelCase_							,   in_channels=config.num_channels							,   out_channels=UpperCAmelCase_							,   kernel_size=3							,   stride=2							,   )

              UpperCamelCase       			=		[1, 2, 1, 2, 1, 2, 1, 1, 1, 1, 1, 2, 1]

              UpperCamelCase       			=		nn.ModuleList()
              for i in range(13      ):
                     UpperCamelCase       			=		out_channels

                     if strides[i] == 2 or i == 0:
                            depth *= 2
                            UpperCamelCase       			=		max(int(depth * config.depth_multiplier      )							,   config.min_depth      )

                     self.layer.append(
                         MobileNetVaConvLayer(
                             UpperCAmelCase_							,   in_channels=UpperCAmelCase_							,   out_channels=UpperCAmelCase_							,   kernel_size=3							,   stride=strides[i]							,   groups=UpperCAmelCase_							,   )      )

                     self.layer.append(
                         MobileNetVaConvLayer(
                             UpperCAmelCase_							,   in_channels=UpperCAmelCase_							,   out_channels=UpperCAmelCase_							,   kernel_size=1							,   )      )

              UpperCamelCase       			=		nn.AdaptiveAvgPoolad((1, 1)      ) if add_pooling_layer else None

              # Initialize weights and apply final processing
              self.post_init()






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Optional[int]							,   UpperCAmelCase_			:				Dict      )->	List[str]:







              """simple docstring"""
              raise NotImplementedError






       @add_start_docstrings_to_model_forward(UpperCAmelCase_      )
       @add_code_sample_docstrings(
           checkpoint=_CHECKPOINT_FOR_DOC							,   output_type=UpperCAmelCase_							,   config_class=_CONFIG_FOR_DOC							,   modality="vision"							,   expected_output=_EXPECTED_OUTPUT_SHAPE							,   )
       def 						_SCREAMING_SNAKE_CASE							(	self			:				Tuple							,   UpperCAmelCase_			:				Optional[torch.Tensor] = None							,   UpperCAmelCase_			:				Optional[bool] = None							,   UpperCAmelCase_			:				Optional[bool] = None							,   )->	Union[tuple, BaseModelOutputWithPoolingAndNoAttention]:







              """simple docstring"""
              UpperCamelCase       			=		(
                  output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
              )
              UpperCamelCase       			=		return_dict if return_dict is not None else self.config.use_return_dict

              if pixel_values is None:
                     raise ValueError("You have to specify pixel_values"      )

              UpperCamelCase       			=		self.conv_stem(UpperCAmelCase_      )

              UpperCamelCase       			=		() if output_hidden_states else None

              for i, layer_module in enumerate(self.layer      ):
                     UpperCamelCase       			=		layer_module(UpperCAmelCase_      )

                     if output_hidden_states:
                            UpperCamelCase       			=		all_hidden_states + (hidden_states,)

              UpperCamelCase       			=		hidden_states

              if self.pooler is not None:
                     UpperCamelCase       			=		torch.flatten(self.pooler(UpperCAmelCase_      )							,   start_dim=1      )
              else:
                     UpperCamelCase       			=		None

              if not return_dict:
                     return tuple(v for v in [last_hidden_state, pooled_output, all_hidden_states] if v is not None      )

              return BaseModelOutputWithPoolingAndNoAttention(
                  last_hidden_state=UpperCAmelCase_							,   pooler_output=UpperCAmelCase_							,   hidden_states=UpperCAmelCase_							,   )





@add_start_docstrings(
    '''
    MobileNetV1 model with an image classification head on top (a linear layer on top of the pooled features), e.g. for
    ImageNet.
    '''     ,  _lowerCAmelCase     ,  )
class 	__a	(   _lowerCAmelCase						):






       def __init__(	self			:				str							,   UpperCAmelCase_			:				MobileNetVaConfig      )->	None:







              """simple docstring"""
              super().__init__(UpperCAmelCase_      )

              UpperCamelCase       			=		config.num_labels
              UpperCamelCase       			=		MobileNetVaModel(UpperCAmelCase_      )

              UpperCamelCase       			=		self.mobilenet_va.layer[-1].convolution.out_channels

              # Classifier head
              UpperCamelCase       			=		nn.Dropout(config.classifier_dropout_prob							,   inplace=UpperCAmelCase_      )
              UpperCamelCase       			=		nn.Linear(UpperCAmelCase_							,   config.num_labels      ) if config.num_labels > 0 else nn.Identity()

              # Initialize weights and apply final processing
              self.post_init()






       @add_start_docstrings_to_model_forward(UpperCAmelCase_      )
       @add_code_sample_docstrings(
           checkpoint=_IMAGE_CLASS_CHECKPOINT							,   output_type=UpperCAmelCase_							,   config_class=_CONFIG_FOR_DOC							,   expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT							,   )
       def 						_SCREAMING_SNAKE_CASE							(	self			:				List[str]							,   UpperCAmelCase_			:				Optional[torch.Tensor] = None							,   UpperCAmelCase_			:				Optional[bool] = None							,   UpperCAmelCase_			:				Optional[torch.Tensor] = None							,   UpperCAmelCase_			:				Optional[bool] = None							,   )->	Union[tuple, ImageClassifierOutputWithNoAttention]:







              """simple docstring"""
              UpperCamelCase       			=		return_dict if return_dict is not None else self.config.use_return_dict

              UpperCamelCase       			=		self.mobilenet_va(UpperCAmelCase_							,   output_hidden_states=UpperCAmelCase_							,   return_dict=UpperCAmelCase_      )

              UpperCamelCase       			=		outputs.pooler_output if return_dict else outputs[1]

              UpperCamelCase       			=		self.classifier(self.dropout(UpperCAmelCase_      )      )

              UpperCamelCase       			=		None
              if labels is not None:
                     if self.config.problem_type is None:
                            if self.num_labels == 1:
                                   UpperCamelCase       			=		"regression"
                            elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
                                   UpperCamelCase       			=		"single_label_classification"
                            else:
                                   UpperCamelCase       			=		"multi_label_classification"

                     if self.config.problem_type == "regression":
                            UpperCamelCase       			=		MSELoss()
                            if self.num_labels == 1:
                                   UpperCamelCase       			=		loss_fct(logits.squeeze()							,   labels.squeeze()      )
                            else:
                                   UpperCamelCase       			=		loss_fct(UpperCAmelCase_							,   UpperCAmelCase_      )
                     elif self.config.problem_type == "single_label_classification":
                            UpperCamelCase       			=		CrossEntropyLoss()
                            UpperCamelCase       			=		loss_fct(logits.view(-1							,   self.num_labels      )							,   labels.view(-1      )      )
                     elif self.config.problem_type == "multi_label_classification":
                            UpperCamelCase       			=		BCEWithLogitsLoss()
                            UpperCamelCase       			=		loss_fct(UpperCAmelCase_							,   UpperCAmelCase_      )

              if not return_dict:
                     UpperCamelCase       			=		(logits,) + outputs[2:]
                     return ((loss,) + output) if loss is not None else output

              return ImageClassifierOutputWithNoAttention(
                  loss=UpperCAmelCase_							,   logits=UpperCAmelCase_							,   hidden_states=outputs.hidden_states							,   )







"""simple docstring"""


import json
import os
import shutil
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path

from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError

from transformers import AutoConfig, BertConfig, GPTaConfig
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import TOKEN, USER, is_staging_test


sys.path.append(str(Path(__file__).parent.parent / """utils"""))

from test_module.custom_configuration import CustomConfig  # noqa E402


SCREAMING_SNAKE_CASE			  =  {
    """return_dict""": False,
    """output_hidden_states""": True,
    """output_attentions""": True,
    """torchscript""": True,
    """torch_dtype""": """float16""",
    """use_bfloat16""": True,
    """tf_legacy_loss""": True,
    """pruned_heads""": {"""a""": 1},
    """tie_word_embeddings""": False,
    """is_decoder""": True,
    """cross_attention_hidden_size""": 128,
    """add_cross_attention""": True,
    """tie_encoder_decoder""": True,
    """max_length""": 50,
    """min_length""": 3,
    """do_sample""": True,
    """early_stopping""": True,
    """num_beams""": 3,
    """num_beam_groups""": 3,
    """diversity_penalty""": 0.5,
    """temperature""": 2.0,
    """top_k""": 10,
    """top_p""": 0.7,
    """typical_p""": 0.2,
    """repetition_penalty""": 0.8,
    """length_penalty""": 0.8,
    """no_repeat_ngram_size""": 5,
    """encoder_no_repeat_ngram_size""": 5,
    """bad_words_ids""": [1, 2, 3],
    """num_return_sequences""": 3,
    """chunk_size_feed_forward""": 5,
    """output_scores""": True,
    """return_dict_in_generate""": True,
    """forced_bos_token_id""": 2,
    """forced_eos_token_id""": 3,
    """remove_invalid_values""": True,
    """architectures""": ["""BertModel"""],
    """finetuning_task""": """translation""",
    """id2label""": {0: """label"""},
    """label2id""": {"""label""": """0"""},
    """tokenizer_class""": """BertTokenizerFast""",
    """prefix""": """prefix""",
    """bos_token_id""": 6,
    """pad_token_id""": 7,
    """eos_token_id""": 8,
    """sep_token_id""": 9,
    """decoder_start_token_id""": 10,
    """exponential_decay_length_penalty""": (5, 1.01),
    """suppress_tokens""": [0, 1],
    """begin_suppress_tokens""": 2,
    """task_specific_params""": {"""translation""": """some_params"""},
    """problem_type""": """regression""",
}



@is_staging_test
class 	__a	(   unittest.TestCase						):






       @classmethod
       def 						_SCREAMING_SNAKE_CASE							(	cls			:				Union[str, Any]      )->	List[Any]:







              """simple docstring"""
              UpperCamelCase       			=		TOKEN
              HfFolder.save_token(UpperCAmelCase_      )






       @classmethod
       def 						_SCREAMING_SNAKE_CASE							(	cls			:				str      )->	Dict:







              """simple docstring"""
              try:
                     delete_repo(token=cls._token							,   repo_id="test-config"      )
              except HTTPError:
                     pass

              try:
                     delete_repo(token=cls._token							,   repo_id="valid_org/test-config-org"      )
              except HTTPError:
                     pass

              try:
                     delete_repo(token=cls._token							,   repo_id="test-dynamic-config"      )
              except HTTPError:
                     pass






       def 						_SCREAMING_SNAKE_CASE							(	self			:				List[Any]      )->	int:







              """simple docstring"""
              UpperCamelCase       			=		BertConfig(
                  vocab_size=99							,   hidden_size=32							,   num_hidden_layers=5							,   num_attention_heads=4							,   intermediate_size=37      )
              config.push_to_hub("test-config"							,   use_auth_token=self._token      )

              UpperCamelCase       			=		BertConfig.from_pretrained(f"{USER}/test-config"      )
              for k, v in config.to_dict().items():
                     if k != "transformers_version":
                            self.assertEqual(UpperCAmelCase_							,   getattr(UpperCAmelCase_							,   UpperCAmelCase_      )      )

        # Reset repo
              delete_repo(token=self._token							,   repo_id="test-config"      )

              # Push to hub via save_pretrained
              with tempfile.TemporaryDirectory() as tmp_dir:
                     config.save_pretrained(UpperCAmelCase_							,   repo_id="test-config"							,   push_to_hub=UpperCAmelCase_							,   use_auth_token=self._token      )

              UpperCamelCase       			=		BertConfig.from_pretrained(f"{USER}/test-config"      )
              for k, v in config.to_dict().items():
                     if k != "transformers_version":
                            self.assertEqual(UpperCAmelCase_							,   getattr(UpperCAmelCase_							,   UpperCAmelCase_      )      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Tuple      )->	List[str]:







              """simple docstring"""
              UpperCamelCase       			=		BertConfig(
                  vocab_size=99							,   hidden_size=32							,   num_hidden_layers=5							,   num_attention_heads=4							,   intermediate_size=37      )
              config.push_to_hub("valid_org/test-config-org"							,   use_auth_token=self._token      )

              UpperCamelCase       			=		BertConfig.from_pretrained("valid_org/test-config-org"      )
              for k, v in config.to_dict().items():
                     if k != "transformers_version":
                            self.assertEqual(UpperCAmelCase_							,   getattr(UpperCAmelCase_							,   UpperCAmelCase_      )      )

        # Reset repo
              delete_repo(token=self._token							,   repo_id="valid_org/test-config-org"      )

              # Push to hub via save_pretrained
              with tempfile.TemporaryDirectory() as tmp_dir:
                     config.save_pretrained(
                         UpperCAmelCase_							,   repo_id="valid_org/test-config-org"							,   push_to_hub=UpperCAmelCase_							,   use_auth_token=self._token      )

              UpperCamelCase       			=		BertConfig.from_pretrained("valid_org/test-config-org"      )
              for k, v in config.to_dict().items():
                     if k != "transformers_version":
                            self.assertEqual(UpperCAmelCase_							,   getattr(UpperCAmelCase_							,   UpperCAmelCase_      )      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				int      )->	Union[str, Any]:







              """simple docstring"""
              CustomConfig.register_for_auto_class()
              UpperCamelCase       			=		CustomConfig(attribute=42      )

              config.push_to_hub("test-dynamic-config"							,   use_auth_token=self._token      )

              # This has added the proper auto_map field to the config
              self.assertDictEqual(config.auto_map							,   {"AutoConfig": "custom_configuration.CustomConfig"}      )

              UpperCamelCase       			=		AutoConfig.from_pretrained(f"{USER}/test-dynamic-config"							,   trust_remote_code=UpperCAmelCase_      )
              # Can't make an isinstance check because the new_config is from the FakeConfig class of a dynamic module
              self.assertEqual(new_config.__class__.__name__							,   "CustomConfig"      )
              self.assertEqual(new_config.attribute							,   42      )





class 	__a	(   unittest.TestCase						):






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Any      )->	Any:







              """simple docstring"""
              UpperCamelCase       			=		GPTaConfig()

              # attempt to modify each of int/float/bool/str config records and verify they were updated
              UpperCamelCase       			=		c.n_embd + 1  # int
              UpperCamelCase       			=		c.resid_pdrop + 1.0  # float
              UpperCamelCase       			=		not c.scale_attn_weights  # bool
              UpperCamelCase       			=		c.summary_type + "foo"  # str
              c.update_from_string(
                  f"n_embd={n_embd},resid_pdrop={resid_pdrop},scale_attn_weights={scale_attn_weights},summary_type={summary_type}"      )
              self.assertEqual(UpperCAmelCase_							,   c.n_embd							,   "mismatch for key: n_embd"      )
              self.assertEqual(UpperCAmelCase_							,   c.resid_pdrop							,   "mismatch for key: resid_pdrop"      )
              self.assertEqual(UpperCAmelCase_							,   c.scale_attn_weights							,   "mismatch for key: scale_attn_weights"      )
              self.assertEqual(UpperCAmelCase_							,   c.summary_type							,   "mismatch for key: summary_type"      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Any      )->	List[str]:







              """simple docstring"""
              UpperCamelCase       			=		PretrainedConfig()
              UpperCamelCase       			=		[key for key in base_config.__dict__ if key not in config_common_kwargs]
              # If this part of the test fails, you have arguments to addin config_common_kwargs above.
              self.assertListEqual(
                  UpperCAmelCase_							,   ["is_encoder_decoder", "_name_or_path", "_commit_hash", "transformers_version"]      )
              UpperCamelCase       			=		[key for key, value in config_common_kwargs.items() if value == getattr(UpperCAmelCase_							,   UpperCAmelCase_      )]
              if len(UpperCAmelCase_      ) > 0:
                     raise ValueError(
                         "The following keys are set with the default values in"
                         " `test_configuration_common.config_common_kwargs` pick another value for them:"
                         f" {', '.join(UpperCAmelCase_      )}."      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Optional[int]      )->	Optional[Any]:







              """simple docstring"""
              with self.assertRaises(UpperCAmelCase_      ):
                     # config is in subfolder, the following should not work without specifying the subfolder
                     UpperCamelCase       			=		BertConfig.from_pretrained("hf-internal-testing/tiny-random-bert-subfolder"      )

              UpperCamelCase       			=		BertConfig.from_pretrained("hf-internal-testing/tiny-random-bert-subfolder"							,   subfolder="bert"      )

              self.assertIsNotNone(UpperCAmelCase_      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Any      )->	Any:







              """simple docstring"""
              # A mock response for an HTTP head request to emulate server down
              UpperCamelCase       			=		mock.Mock()
              UpperCamelCase       			=		500
              UpperCamelCase       			=		{}
              UpperCamelCase       			=		HTTPError
              UpperCamelCase       			=		{}

              # Download this model to make sure it's in the cache.
              UpperCamelCase       			=		BertConfig.from_pretrained("hf-internal-testing/tiny-random-bert"      )

              # Under the mock environment we get a 500 error when trying to reach the model.
              with mock.patch("requests.Session.request"							,   return_value=UpperCAmelCase_      ) as mock_head:
                     UpperCamelCase       			=		BertConfig.from_pretrained("hf-internal-testing/tiny-random-bert"      )
                     # This check we did call the fake head request
                     mock_head.assert_called()






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Tuple      )->	int:







              """simple docstring"""
              # This test is for deprecated behavior and can be removed in v5
              UpperCamelCase       			=		BertConfig.from_pretrained(
                  "https://huggingface.co/hf-internal-testing/tiny-random-bert/resolve/main/config.json"      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Union[str, Any]      )->	Optional[int]:







              """simple docstring"""
              UpperCamelCase       			=		AutoConfig.from_pretrained("bert-base-cased"      )
              UpperCamelCase       			=		["config.4.0.0.json"]

              with tempfile.TemporaryDirectory() as tmp_dir:
                     configuration.save_pretrained(UpperCAmelCase_      )
                     UpperCamelCase       			=		2
                     json.dump(configuration.to_dict()							,   open(os.path.join(UpperCAmelCase_							,   "config.4.0.0.json"      )							,   "w"      )      )

                     # This should pick the new configuration file as the version of Transformers is > 4.0.0
                     UpperCamelCase       			=		AutoConfig.from_pretrained(UpperCAmelCase_      )
                     self.assertEqual(new_configuration.hidden_size							,   2      )

                     # Will need to be adjusted if we reach v42 and this test is still here.
                     # Should pick the old configuration file as the version of Transformers is < 4.42.0
                     UpperCamelCase       			=		["config.42.0.0.json"]
                     UpperCamelCase       			=		768
                     configuration.save_pretrained(UpperCAmelCase_      )
                     shutil.move(os.path.join(UpperCAmelCase_							,   "config.4.0.0.json"      )							,   os.path.join(UpperCAmelCase_							,   "config.42.0.0.json"      )      )
                     UpperCamelCase       			=		AutoConfig.from_pretrained(UpperCAmelCase_      )
                     self.assertEqual(new_configuration.hidden_size							,   768      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Optional[Any]      )->	Dict:







              """simple docstring"""
              # This repo has two configuration files, one for v4.0.0 and above with a different hidden size.
              UpperCamelCase       			=		"hf-internal-testing/test-two-configs"

              import transformers as new_transformers

              UpperCamelCase       			=		"v4.0.0"
              UpperCamelCase			,							UpperCamelCase       			=		new_transformers.models.auto.AutoConfig.from_pretrained(
                  UpperCAmelCase_							,   return_unused_kwargs=UpperCAmelCase_      )
              self.assertEqual(new_configuration.hidden_size							,   2      )
              # This checks `_configuration_file` ia not kept in the kwargs by mistake.
              self.assertDictEqual(UpperCAmelCase_							,   {}      )

              # Testing an older version by monkey-patching the version in the module it's used.
              import transformers as old_transformers

              UpperCamelCase       			=		"v3.0.0"
              UpperCamelCase       			=		old_transformers.models.auto.AutoConfig.from_pretrained(UpperCAmelCase_      )
              self.assertEqual(old_configuration.hidden_size							,   768      )





